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Representing Both First- and Second-order Uncertainties by Monte Carlo Simulation for Groups of Patients

Author

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  • Elkan F. Halpern
  • Milton C. Weinstein
  • Maria G.M. Hunink
  • G. Scott Gazelle

Abstract

Actual implementation of probabilistic sensitivity analysis may lead to misleading or improper conclusions when it is applied to groups of patients rather than individual patients. The practice of combining first- and second-order simulations when modeling the outcome for a group of more than one patient yields an erroneous marginal distribution whenever the parameter values are randomly sampled for each patient while the results are presented as simulated means for the group of patients. This practice results in underrepresenting the second-order uncertainty. It may also distort the shape (especially the symmetry or extent of the tails) in the simulated distribution. As a result, it may lead to premature or incorrect conclusions of superiority. It may also result in inappropriate estimates of the value of further research to inform parameter values. Key words: Simulation; probabilistic sensitivity analysis; Monte Carlo; confidence interval ; cost-effectiveness; implementation. (Med Decis Making 2000;20:314-322)

Suggested Citation

  • Elkan F. Halpern & Milton C. Weinstein & Maria G.M. Hunink & G. Scott Gazelle, 2000. "Representing Both First- and Second-order Uncertainties by Monte Carlo Simulation for Groups of Patients," Medical Decision Making, , vol. 20(3), pages 314-322, July.
    • Handle: RePEc:sae:medema:v:20:y:2000:i:3:p:314-322
    DOI: 10.1177/0272989X0002000308
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    1. Neale Cohen & Michael Minshall & Lyn Sharon-Nash & Katerina Zakrzewska & William Valentine & Andrew Palmer, 2007. "Continuous Subcutaneous Insulin Infusion versus Multiple Daily Injections of Insulin," PharmacoEconomics, Springer, vol. 25(10), pages 881-897, October.
    2. Elizabeth G Bond & Lusine Abrahamyan & Mohammad K A Khan & Andrea Gershon & Murray Krahn & Ping Li & Rajibul Mian & Nicholas Mitsakakis & Mohsen Sadatsafavi & Teresa To & Petros Pechlivanoglou & for t, 2020. "Understanding resource utilization and mortality in COPD to support policy making: A microsimulation study," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-16, August.
    3. Cannon, Jeffrey W. & Mueller, Ute A. & Hornbuckle, Janet & Larson, Ann & Simmer, Karen & Newnham, John P. & Doherty, Dorota A., 2013. "Economic implications of poor access to antenatal care in rural and remote Western Australian Aboriginal communities: An individual sampling model of pregnancy," European Journal of Operational Research, Elsevier, vol. 226(2), pages 313-324.
    4. Sandra Tunis & Luc Sauriol & Michael Minshall, 2010. "Cost effectiveness of insulin glargine plus oral antidiabetes drugs compared with premixed insulin alone in patients with type 2 diabetes mellitus in Canada," Applied Health Economics and Health Policy, Springer, vol. 8(4), pages 267-280, July.
    5. Sankararaman, S. & Mahadevan, S., 2013. "Separating the contributions of variability and parameter uncertainty in probability distributions," Reliability Engineering and System Safety, Elsevier, vol. 112(C), pages 187-199.
    6. Sankararaman, Shankar & Mahadevan, Sankaran, 2011. "Likelihood-based representation of epistemic uncertainty due to sparse point data and/or interval data," Reliability Engineering and System Safety, Elsevier, vol. 96(7), pages 814-824.
    7. Sun-Young Kim & Sue Goldie, 2008. "Cost-Effectiveness Analyses of Vaccination Programmes," PharmacoEconomics, Springer, vol. 26(3), pages 191-215, March.
    8. Luyi Li & Zhenzhou Lu, 2016. "A new algorithm for importance analysis of the inputs with distribution parameter uncertainty," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(13), pages 3065-3077, October.
    9. Sarazin, Gabriel & Morio, Jérôme & Lagnoux, Agnès & Balesdent, Mathieu & Brevault, Loïc, 2021. "Reliability-oriented sensitivity analysis in presence of data-driven epistemic uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    10. Montserrat Vera-Llonch & Ellen Dukes & Javier Rejas & Oleg Sofrygin & Marko Mychaskiw & Gerry Oster, 2010. "Cost-effectiveness of pregabalin versus venlafaxine in the treatment of generalized anxiety disorder: findings from a Spanish perspective," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 11(1), pages 35-44, February.
    11. Marta O. Soares & Luísa Canto e Castro, 2012. "Continuous Time Simulation and Discretized Models for Cost-Effectiveness Analysis," PharmacoEconomics, Springer, vol. 30(12), pages 1101-1117, December.
    12. Vinaytosh Mishra & Mohita G. Sharma, 2020. "Understanding Humanitarian Supply Chain Through Causal Modelling," South Asian Journal of Business and Management Cases, , vol. 9(3), pages 317-329, December.
    13. Niklas Zethraeus & Magnus Johannesson & Bengt Jönsson & Mickael Löthgren & Magnus Tambour, 2003. "Advantages of Using the Net-Benefit Approach for Analysing Uncertainty in Economic Evaluation Studies," PharmacoEconomics, Springer, vol. 21(1), pages 39-48, January.
    14. Mehdi Najafzadeh & Carlo Marra & Eleni Galanis & David Patrick, 2009. "Cost Effectiveness of Herpes Zoster Vaccine in Canada," PharmacoEconomics, Springer, vol. 27(12), pages 991-1004, December.
    15. Marta Soares & Luísa Canto e Castro, 2012. "Continuous Time Simulation and Discretized Models for Cost-Effectiveness Analysis," PharmacoEconomics, Springer, vol. 30(12), pages 1101-1117, December.
    16. Marta O Soares & L Canto e Castro, 2010. "Simulation or cohort models? Continuous time simulation and discretized Markov models to estimate cost-effectiveness," Working Papers 056cherp, Centre for Health Economics, University of York.

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